Detection method of wind speed anomaly fluctuation based on SSA−LSTM
Aiming at the problem of high leakage rate and false alarm rate of traditional statistical methods for abnormal fluctuation in sensor monitoring data caused by dampers opening and closing, a SSA-LSTM wind speed abnormal fluctuation detection method based on the combination of Singular Spectrum Analy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Coal Science and Technology
2024-03-01
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| Series: | Meitan kexue jishu |
| Subjects: | |
| Online Access: | http://www.mtkxjs.com.cn/article/doi/10.12438/cst.2023-0463 |
| _version_ | 1827276799785041920 |
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| author | Lijun DENG Jinbo YUAN Jian LIU Wentian SHANG |
| author_facet | Lijun DENG Jinbo YUAN Jian LIU Wentian SHANG |
| author_sort | Lijun DENG |
| collection | DOAJ |
| description | Aiming at the problem of high leakage rate and false alarm rate of traditional statistical methods for abnormal fluctuation in sensor monitoring data caused by dampers opening and closing, a SSA-LSTM wind speed abnormal fluctuation detection method based on the combination of Singular Spectrum Analysis (SSA) and Long and Short-Term Memory Neural Network (LSTM) was proposed by mining the data features in the time-series data in the wind speed sensors. Firstly, SSA was used to pre-process the wind speed sensor monitoring data, and the wind speed data was decomposed into trend component, periodic component and noise component. The data noise generated by turbulent pulsation was removed via reorganizing the trend component and noise component. The LSTM parameters was then optimized, and the optimized LSTM model was used to predict the pre-processed data and obtain the reconstructed wind speed. Finally, the anomaly fraction of the monitored wind speed and reconstructed wind speed was calculated by using the logarithmic probability density function. Anomaly detection for monitoring wind speed was performed by calculating the threshold set value of training set data samples. The experimental results shown that, the removing effect for the data noise generated by turbulence pulsation via SSA was better. Removing the noise component without affecting the data fluctuation was helpful in improving the wind speed reconstruction effect and the anomaly detection accuracy. LSTM can correctly reconstruct the small amplitude wave due to turbulence pulsation without anomalous fluctuation and fits well with the actual data. The reconstruction of abnormal fluctuation segment based on historical fluctuation trend when there was abnormal fluctuation can effectively improve the accuracy of anomaly detection. Through comparative analysis, the reconstruction effect of proposed method in this paper was better than ARIMA, BP and CNN models, with an anomaly detection accuracy of 99.2% and an F1-Score of 0.97, which verified the reliability of the proposed method. The method proposed in the paper has important application value in detecting the abnormal fluctuation of wind speed caused by the opening and closing of dampers. |
| first_indexed | 2024-04-24T07:05:53Z |
| format | Article |
| id | doaj.art-da7cbacfb66e452a8be5879d66928085 |
| institution | Directory Open Access Journal |
| issn | 0253-2336 |
| language | zho |
| last_indexed | 2024-04-24T07:05:53Z |
| publishDate | 2024-03-01 |
| publisher | Editorial Department of Coal Science and Technology |
| record_format | Article |
| series | Meitan kexue jishu |
| spelling | doaj.art-da7cbacfb66e452a8be5879d669280852024-04-22T03:20:42ZzhoEditorial Department of Coal Science and TechnologyMeitan kexue jishu0253-23362024-03-0152313914710.12438/cst.2023-04632023-0463Detection method of wind speed anomaly fluctuation based on SSA−LSTMLijun DENG0Jinbo YUAN1Jian LIU2Wentian SHANG3College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, ChinaCollege of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, ChinaCollege of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, ChinaCollege of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, ChinaAiming at the problem of high leakage rate and false alarm rate of traditional statistical methods for abnormal fluctuation in sensor monitoring data caused by dampers opening and closing, a SSA-LSTM wind speed abnormal fluctuation detection method based on the combination of Singular Spectrum Analysis (SSA) and Long and Short-Term Memory Neural Network (LSTM) was proposed by mining the data features in the time-series data in the wind speed sensors. Firstly, SSA was used to pre-process the wind speed sensor monitoring data, and the wind speed data was decomposed into trend component, periodic component and noise component. The data noise generated by turbulent pulsation was removed via reorganizing the trend component and noise component. The LSTM parameters was then optimized, and the optimized LSTM model was used to predict the pre-processed data and obtain the reconstructed wind speed. Finally, the anomaly fraction of the monitored wind speed and reconstructed wind speed was calculated by using the logarithmic probability density function. Anomaly detection for monitoring wind speed was performed by calculating the threshold set value of training set data samples. The experimental results shown that, the removing effect for the data noise generated by turbulence pulsation via SSA was better. Removing the noise component without affecting the data fluctuation was helpful in improving the wind speed reconstruction effect and the anomaly detection accuracy. LSTM can correctly reconstruct the small amplitude wave due to turbulence pulsation without anomalous fluctuation and fits well with the actual data. The reconstruction of abnormal fluctuation segment based on historical fluctuation trend when there was abnormal fluctuation can effectively improve the accuracy of anomaly detection. Through comparative analysis, the reconstruction effect of proposed method in this paper was better than ARIMA, BP and CNN models, with an anomaly detection accuracy of 99.2% and an F1-Score of 0.97, which verified the reliability of the proposed method. The method proposed in the paper has important application value in detecting the abnormal fluctuation of wind speed caused by the opening and closing of dampers.http://www.mtkxjs.com.cn/article/doi/10.12438/cst.2023-0463abnormal fluctutionsdampers opening and closinganomaly detectionssalstmtime-series |
| spellingShingle | Lijun DENG Jinbo YUAN Jian LIU Wentian SHANG Detection method of wind speed anomaly fluctuation based on SSA−LSTM Meitan kexue jishu abnormal fluctutions dampers opening and closing anomaly detection ssa lstm time-series |
| title | Detection method of wind speed anomaly fluctuation based on SSA−LSTM |
| title_full | Detection method of wind speed anomaly fluctuation based on SSA−LSTM |
| title_fullStr | Detection method of wind speed anomaly fluctuation based on SSA−LSTM |
| title_full_unstemmed | Detection method of wind speed anomaly fluctuation based on SSA−LSTM |
| title_short | Detection method of wind speed anomaly fluctuation based on SSA−LSTM |
| title_sort | detection method of wind speed anomaly fluctuation based on ssa lstm |
| topic | abnormal fluctutions dampers opening and closing anomaly detection ssa lstm time-series |
| url | http://www.mtkxjs.com.cn/article/doi/10.12438/cst.2023-0463 |
| work_keys_str_mv | AT lijundeng detectionmethodofwindspeedanomalyfluctuationbasedonssalstm AT jinboyuan detectionmethodofwindspeedanomalyfluctuationbasedonssalstm AT jianliu detectionmethodofwindspeedanomalyfluctuationbasedonssalstm AT wentianshang detectionmethodofwindspeedanomalyfluctuationbasedonssalstm |